A paradox of syntactic priming: why response tendencies show priming for passives, and response latencies show priming for actives

Speakers tend to repeat syntactic structures across sentences, a phenomenon called syntactic priming. Although it has been suggested that repeating syntactic structures should result in speeded responses, previous research has focused on effects in response tendencies. We investigated syntactic priming effects simultaneously in response tendencies and response latencies for active and passive transitive sentences in a picture description task. In Experiment 1, there were priming effects in response tendencies for passives and in response latencies for actives. However, when participants' pre-existing preference for actives was altered in Experiment 2, syntactic priming occurred for both actives and passives in response tendencies as well as in response latencies. This is the first investigation of the effects of structure frequency on both response tendencies and latencies in syntactic priming. We discuss the implications of these data for current theories of syntactic processing

Active Learning with Statistical Models

For many types of machine learning algorithms, one can compute the statistically `optimal' way to select training data. In this paper, we review how optimal data selection techniques have been used with feedforward neural networks. We then show how the same principles may be used to select data for two alternative, statistically-based learning architectures: mixtures of Gaussians and locally weighted regression. While the techniques for neural networks are computationally expensive and approximate, the techniques for mixtures of Gaussians and locally weighted regression are both efficient and accurate. Empirically, we observe that the optimality criterion sharply decreases the number of training examples the learner needs in order to achieve good performance.Comment: See http://www.jair.org/ for any accompanying file

Carrots without Sticks: The Impacts of Job Search Assistance in a Regime with Minimal Monitoring and Sanctions. ESRI WP409, September 2011

This paper uses a high quality longitudinal dataset to assess the impact of an active labour market intervention consisting of referral for interview plus Job Search Assistance (JSA) with the public employment service in Ireland during a period when both job search monitoring and sanctions were virtually non‐existent. The results indicate that, relative to a control group with no intervention, unemployed individuals that were exposed to the interview letter and participated in JSA were 16 per cent less likely to have exited to employment prior to 12 months. The negative effects of the intervention approximately doubled when those that received a referral letter but did not attend a JSA interview were removed from the data. The results held when tested against the underlying assumptions of the model, and the influences of both sample selection and unobserved heterogeneity bias. The negative treatment impact is attributed to individuals lowering their job search intensity on learning, through the JSA activation interview, of the lax nature of the activation process. The research, which is unusual in the international literature in allowing the assessment of the impact of job search assistance in the virtual absence of monitoring and sanctions, highlights the need for effective monitoring and sanctions as integral components of labour market activation programmes

Can hierarchical predictive coding explain binocular rivalry?

Hohwy et al.’s (2008) model of binocular rivalry (BR) is taken as a classic illustration of predictive coding’s explanatory power. I revisit the account and show that it cannot explain the role of reward in BR. I then consider a more recent version of Bayesian model averaging, which recasts the role of reward in (BR) in terms of optimism bias. If we accept this account, however, then we must reconsider our conception of perception. On this latter view, I argue, organisms engage in what amounts to policy-driven, motivated perception

Automatic Curriculum Learning For Deep RL: A Short Survey

Automatic Curriculum Learning (ACL) has become a cornerstone of recent successes in Deep Reinforcement Learning (DRL).These methods shape the learning trajectories of agents by challenging them with tasks adapted to their capacities. In recent years, they have been used to improve sample efficiency and asymptotic performance, to organize exploration, to encourage generalization or to solve sparse reward problems, among others. The ambition of this work is dual: 1) to present a compact and accessible introduction to the Automatic Curriculum Learning literature and 2) to draw a bigger picture of the current state of the art in ACL to encourage the cross-breeding of existing concepts and the emergence of new ideas.Comment: Accepted at IJCAI202

CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning

In open-ended environments, autonomous learning agents must set their own goals and build their own curriculum through an intrinsically motivated exploration. They may consider a large diversity of goals, aiming to discover what is controllable in their environments, and what is not. Because some goals might prove easy and some impossible, agents must actively select which goal to practice at any moment, to maximize their overall mastery on the set of learnable goals. This paper proposes CURIOUS, an algorithm that leverages 1) a modular Universal Value Function Approximator with hindsight learning to achieve a diversity of goals of different kinds within a unique policy and 2) an automated curriculum learning mechanism that biases the attention of the agent towards goals maximizing the absolute learning progress. Agents focus sequentially on goals of increasing complexity, and focus back on goals that are being forgotten. Experiments conducted in a new modular-goal robotic environment show the resulting developmental self-organization of a learning curriculum, and demonstrate properties of robustness to distracting goals, forgetting and changes in body properties.Comment: Accepted at ICML 201